Name
Abstract | ||
|---|---|---|
Variability in single neuron models is typically implemented either by a stochastic Leaky-Integrate-and-Fire model or by a model of the Generalized Linear Model (GLM) family. We use analytical and numerical methods to relate state-of-the-art models from both schools of thought. First we find the analytical expressions relating the subthreshold voltage from the Adaptive Exponential Integrate-and-Fire model (AdEx) to the Spike-Response Model with escape noise (SRM as an example of a GLM). Then we calculate numerically the link-function that provides the firing probability given a deterministic membrane potential. We find a mathematical expression for this link-function and test the ability of the GLM to predict the firing probability of a neuron receiving complex stimulation. Comparing the prediction performance of various link-functions, we find that a GLM with an exponential link-function provides an excellent approximation to the Adaptive Exponential Integrate-and-Fire with colored-noise input. These results help to understand the relationship between the different approaches to stochastic neuron models. |
Year | Venue | Field |
|---|---|---|
2011 | NIPS | Nonlinear system,Exponential function,Expression (mathematics),Generalized linear model,Subthreshold conduction,Artificial intelligence,Numerical analysis,Machine learning,Mathematics |
DocType | Citations | PageRank |
Conference | 6 | 0.51 |
References | Authors | |
7 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mensi, Skander | 1 | 37 | 2.39 |
| Richard Naud | 2 | 140 | 9.28 |
| Wulfram Gerstner | 3 | 2437 | 410.08 |